Understanding Mental Health Help-Seeking: A Discrete Choice Experiment

Mental health help-seeking is shaped by various factors that determine whether individuals ultimately decide to pursue treatment. The present study aims to examine preferences in mental health service selection by identifying which attributes most strongly influence decision-making. Using a discrete choice experiment (DCE), we will experimentally manipulate key attributes of mental health services, including treatment type, appointment frequency, wait time, treatment effectiveness, and the ability to evaluate therapeutic alliance. DCE will allow us to understand how individuals make trade-offs when help-seeking. A community sample will be recruited through Prolific and will complete a series of choice tasks comparing pairs of hypothetical service options that vary across these attributes. Results will determine the relative importance of each attribute and the features that are important when people are seeking services. Understanding which aspects of mental health services are most valued, and how individuals prioritize these factors, will contribute to improving service design and informing interventions that better align with people’s needs and preferences.

Hello, my name is Griffin Perry, and I am a current Junior at George Mason University, double-majoring in psychology and community health. For my Fall 2025 URSP project, I sought to examine individuals’ preferences when mental health help-seeking by developing a discrete choice experiment.

Mental health help-seeking refers to the process of an individual recognizing that they are facing psychological distress, deciding that they need help for this problem, and then taking action to get mental health support from resources available.

However, seeking services is highly complex, with multiple factors influencing whether someone chooses to seek services. To better understand these decisions, we decided to develop a discrete choice experiment, or DCE for short.

DCEs are a quantitative research method used to examine how people make decisions by presenting them with experimentally manipulated hypothetical scenarios. These scenarios contain different combinations of attributes and ask participants to choose which option they prefer.

Attributes are the specific features or characteristics of a service or option that may influence an individual’s decision-making. In a DCE, each attribute is assigned a set of levels, aka variations that reflect realistic possibilities a person might encounter.

For example, in the context of mental health services, attributes might include wait time. Then this attribute would be broken down into different attribute levels. So, for this example, the possible levels for wait time could be two weeks, a month, or three months.

This approach captures the complexity of help-seeking more accurately than traditional surveys, offering more profound insight into the barriers and facilitators that shape whether someone ultimately decides to access mental health care.

For the duration of this URSP, we developed attributes, their levels, and wrote the IRB to collect data. We created attributes based on past literature. For our project, we will use the following attributes:

Treatment type which refers to the specific form of mental health care being offered, such as individual therapy or group therapy.

The frequency of appointment, which is just how often a client meets with a mental health provider

The wait time which refers to the length of time a person must wait before beginning treatment.

The effectiveness of treatment, which refers to the expected likelihood that the treatment will lead to meaningful improvement

And finally, the ability to evaluate therapeutic alliance, which is the extent to which an individual can assess the quality of their relationship with a provider before committing to ongoing care.

Here is an example to help conceptualize what our study would look like for our participants. As stated previously, participants will choose their preferred option of the two.

We intend to use Prolific over winter break to collect a community sample. Prolific is an online research platform that connects researchers with participants all over the nation. Through Prolific, we can recruit a diverse group of participants in the community.

Once we have collected our sample, we will be able to determine which attributes are most important, how individuals trade off between different aspects of a service, and what factors most strongly influence their decisions.

Results will allow us to use a novel method to better understand the specific factors that shape individuals’ preferences when seeking mental health care by identifying which attributes carry the most weight in decision-making.

A big thanks to Dr. Natasha Tonge, Gracie Kelly, and Dr. Karen Lee for supporting me through this project.